Color-to-Grayscale Image Conversion Based on the Entropy and the Local Contrast

Color-to-grayscale conversion is a fundamental preprocessing task with widespread applications in digital printing, electronic ink displays, medical imaging, and artistic photo stylization. A primary challenge in this domain is to simultaneously preserve global luminance distribution and local contrast. To address this, we propose an adaptive conversion method centered on a novel objective function that integrates information entropy with Edge Content (EC), a metric for local gradient information. The key advantage of our approach is its ability to generate grayscale results that maintain both rich overall contrast and fine-grained local details. Compared with previous adaptive linear methods, our approach demonstrates superior qualitative and quantitative performance. Furthermore, by eliminating the need for computationally expensive edge detection, the proposed algorithm provides an effective solution to the color-to-grayscale conversion.